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Interactive Histology of Large-Scale Biomedical Image Stacks

Won-Ki Jeong, Jens Schneider, Stephen G. Turney, Beverly E. Faulkner-Jones, Dominik Meyer, Rudiger Westermann, R. Clay Reid, Jeff Lichtman, Hanspeter Pfister

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Tags: Image processing, Medicine, nVidia, nVidia GeForce GTX 285, OpenGL, Texture detection, Visualization

June 17, 2011 by hgpu

Multi-scale neural texture classification using the GPU as a stream processing engine

M. Martinez-Zarzuela, F. Diaz-Pernas, M. Anton-Rodriguez, J. Diez-Higuera, D. Gonzalez-Ortega, D. Boto-Giralda, F. Lopez-Gonzalez, I. De La Torre

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Tags: Cg, Computer science, nVidia, nVidia GeForce 8800 GTX, OpenGL, Texture detection

November 16, 2010 by hgpu

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

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